2. The Role of Background Knowledge in Sentence Processing Raluca Budiu July 9, 2001 Thesis Committee: John Anderson, Chair Jaime Carbonell David Plaut Lynne Reder, Department of Psychology

3. Thesis Defense, July 9, 20012 Ambiguity of Language My mouse behaves erratically lately. -- From an e-mail to CS facilities Could you pass me the salt? That's the sun of the egg. -- Child speaking about the yolk of a fried egg

4. Thesis Defense, July 9, 20013 Language and Noise Communication channels are noisy People make mistakes We understand how unfair the death penalty is. -- George W. Bush, speaking of death tax Listeners ignore semantic inconsistencies When an aircraft crashes, where should the survivors be buried?

5. Thesis Defense, July 9, 20014 Insight from this Research Flexibility: stretching words meanings Reliability: ignoring noise & semantic inconsistencies

6. Thesis Defense, July 9, 20015 The Sentence-Processing Model Model Prior knowledge Sentence Noah took two animals of each kind on the ark Napoleon was defeated at Waterloo in 1815 Plato was Socrates student Sentence interpretation

7. Thesis Defense, July 9, 20016 Main Contribution A model of language comprehension that: Offers a unified explanation of several complex linguistic phenomena Is incremental (on line) Is as fast as humans Uses prior knowledge and sentence context to understand vague words Is based on the ACT-R theory (Anderson & Lebiere, 1998)

8. Thesis Defense, July 9, 20017 ACT-R A cognitive architecture based on production systems A rigorous framework for building, running and testing computational models Based on verified assumptions about human cognition (e.g., memory properties, attention) Produces quantitative predictions about human behavior (e.g., accuracy and latency in a task)

9. Thesis Defense, July 9, 20018 Research Methodology Human subjects Experiment Match? Quantitative measures Computational ACT-R model predictions no

10. Thesis Defense, July 9, 20019 Evaluation of the Model The model Can comprehend –Literal or metaphoric, distorted or undistorted sentences –Isolated or in-discourse sentences Can explain patterns of text recall Compares well with people on psycholinguistic experiments Is fast, accurate, and scalable

11. Thesis Defense, July 9, 200110 Outline Introduction The sentence-processing model –Evaluation Comprehension of sentences in discourse –Evaluation Scalability Future work and conclusions

12. Thesis Defense, July 9, 200111 The Sentence-Processing Model Model Background knowledge (words + thematic roles) Noah took two animals of each kind on the ark Napoleon was defeated at Waterloo in 1815 Plato was Socrates student Sentence interpretation Input sentence

13. Thesis Defense, July 9, 200112 Propositional Representation take arkanimals Noah Ark Prop agent verb place-oblique patient Parent Ark Prop Child animals Type patient Noah took the animals on the ark

14. Thesis Defense, July 9, 200113 Associations Noah took the animals on the ark Napoleon was defeated at Waterloo Noah is Lamechs son Patriarch Noah Moses Napoleon take Noah & Activation

15. Thesis Defense, July 9, 200114 Noah is Lamechs son Noah took the animals on the ark Noah is Lamechs son Noah took the animals on the ark Noah Search took the animals on the arkNoah Napoleon was defeated at Waterloo Patriarch Moses Napoleon take

16. Thesis Defense, July 9, 200115 Noah Match took the animals on the arkNoah Noah took the animals on the ark Napoleon was defeated at Waterloo Noah is Lamechs son Patriarch Moses Napoleon take Noah is Lamechs sonNoah

17. Thesis Defense, July 9, 200116 Noah Match took the animals on the arkNoah Noah took the animals on the ark Napoleon was defeated at Waterloo Noah is Lamechs son Patriarch Moses Napoleon take Noah is Lamechs son is took take

18. Thesis Defense, July 9, 200117 Noah Search took the animals on the arkNoah Noah took the animals on the ark Napoleon was defeated at Waterloo Noah is Lamechs son Patriarch Moses Napoleon take took take Noah took the animals on the ark

19. Thesis Defense, July 9, 200118 Final Interpretation Noah took the animals on the ark Napoleon was defeated at Waterloo Noah is Lamechs son Patriarch Noah Moses Napoleon take

20. Thesis Defense, July 9, 200119 Failures of Comprehension burnt offerings on the altar Lamech Prop Noahoffered Lamech Prop No interpretation word offered role verb interpretation Lamech Prop Bug

21. Thesis Defense, July 9, 200120 Summary of the Model Read word Bug no Integration end of sentence Interpretation? yes Search no Match? yes

22. Thesis Defense, July 9, 200121 Answering True/False Queries False = a bug OR no final interpretation found True = no bug AND final interpretation found

23. Thesis Defense, July 9, 200122 Outline Introduction The sentence-processing model –Empirical evaluation Moses illusion Metaphor-position effects Comprehension of sentences in discourse –Evaluation Scalability Future work and conclusions

24. Thesis Defense, July 9, 200123 Moses Illusion How many animals of each kind did Moses take on the ark? Good vs. bad distortions How many animals of each kind did Adam take on the ark?

25. Thesis Defense, July 9, 200124 Moses-Illusion Data Illusion rates for good and bad distortions (Ayers, Reder & Anderson, 1996) Percent correct distortions in the gist task (Ayers et al., 1996) Reading times in the literal and gist task (Reder & Kusbit, 1991)

26. Thesis Defense, July 9, 200125

27. Thesis Defense, July 9, 200126 Moses Noah Good Distortions take on the ark animals Noah took the animals on the ark Napoleon was defeated at Waterloo Joe raises animals Moses Napoleon take How manydid Moses Adam animals Noah took the animals on the ark

28. Thesis Defense, July 9, 200127 Adam Noah Bad Distortions take on the ark animals Noah took the animals on the ark Napoleon was defeated at Waterloo Joe raises animals Moses Napoleon take How many did Adam animals Noah took the animals on the ark Adam No interpretation Bug

29. Thesis Defense, July 9, 200128 Simulation of Moses Illusion take ark animals Noah Ark Prop agent verb place-oblique patient Moses Adam How many animals did Moses take on the ark Zoo Prop Ark Prop Adam Zoo Prop No interpretation Bug

30. Thesis Defense, July 9, 200129 Metaphor Comprehension Effects of position on metaphor understanding (Gerrig & Healy, 1983) Metaphor-familiarity effects (Budiu & Anderson, 1999) Understanding metaphoric/literal sentences in context (Budiu & Anderson, 2000)

31. Thesis Defense, July 9, 200130 Metaphor Position Stars Prop Container Prop Stars Prop Drops of molten silver filledthe night sky. The night sky was filled with drops of molten silver. 4.30 s 3.68 s Model 3.53 s 4.21 s Humans

32. Thesis Defense, July 9, 200131 Outline Introduction The sentence-processing model –Evaluation Comprehension of sentences in discourse –Evaluation Scalability Future work and conclusions

33. Thesis Defense, July 9, 200132 Sentences in Discourse Create background knowledge from discourse propositions King Lear had three daughters Goneril and Regan declare their grand love King Lear decided to divide his kingdom Cordelia is disinherited … Cordelia refuses to make an insincere speech Cordelia marries the king of France King Lears story

34. Thesis Defense, July 9, 200133 Novel Sentences Use a partially matching interpretation to relate to discourse Cordelia marries the king of France Prop 5 Cordelia is disinherited No interpretationProp 5 No interpretation word married Interpretation Prop 5 …… Bug Integration Prop 5

35. Thesis Defense, July 9, 200134 Outline Introduction The sentence-processing model –Evaluation Comprehension of sentences in discourse Evaluation Scalability Future work and conclusions

36. Thesis Defense, July 9, 200135 Metaphor in Discourse ExperimentsMetaphor vs. Literal Reading Time Ortony et al., 1978 Inhoff et al., 1984 Shinjo & Myers, 1987 Keysar, 1990 Gibbs, 1990 Onishi & Murphy, 1993 slower same Comprehension shallow deep Answer true/false Comprehension (of novel sentences) Our Experiments

37. Thesis Defense, July 9, 200136 Metaphoric Sentences in Context During history seminars, a massive young man always yawned and never paid any attention to the discussions. He was a very good linebacker who had been all-state in football. The seminar always came after his training sessions, so he was very tired. The bear slept quietlyThe bear yawned in class Read new: The athlete slept quietlyThe athlete yawned in class True or false:

38. Thesis Defense, July 9, 200137 Metaphoric Sentences in Context The bear yawned in class True or false: Find interpretation Bug Reevaluate bug The athlete yawned in class Find interpretation The athlete slept quietly Interpretation No interpretation Bug-based integration The bear slept quietly Read new: No interpretation bear Bug

39. Thesis Defense, July 9, 200138 Outline Introduction The sentence-processing model Evaluation Comprehension of sentences in discourse Evaluation Scalability Future work and conclusions

40. Thesis Defense, July 9, 200139 Computational Constraints Speed Accuracy Scalability - Word database Sentence database

41. Thesis Defense, July 9, 200140 Scalability Test 436 noun-verb-noun sentences (Brown corpus via PennTreebank project) 999 distinct words One word repeated in at most 9 propositions Associations based on LSA similarity measures (Landauer & Dumais, 1997) Test for comprehension of a known sentence

42. Thesis Defense, July 9, 200141 Model Performance ExperimentAccuracySwitches/word Metaphor position Moses illusion literal gist> 94%< 0.72 Metaphor verification Metaphor comprehension Text Memory

43. Thesis Defense, July 9, 200142 Summary A model of sentence comprehension with a strong associative mechanism to speed up the search of an interpretation It offers a unified explanation for a variety of empirical psycholinguistic data It is scalable It is implemented in ACT-R

44. Thesis Defense, July 9, 200143 Future Work Extend the model to other empirical phenomena (e.g., priming, text inference, lexical ambiguity) Identify the ACT-R assumptions that are fundamental Eliminate some of the limitations

45. Thesis Defense, July 9, 200144 Conclusions Context can help the comprehension of metaphoric or semantically-flawed sentences Semantic associations between words are a powerful mechanism that allows fast and flexible comprehension Peripheral language phenomena can shed light on deep cognitive processes

46. Thesis Defense, July 9, 200145 Limitations of the Model No syntactic processing Atomic word-phrases (e.g., drops of molten silver) Rudimentary discourse processing Cannot account for sentences containing similar words (e.g., George W.Bush is the son of George Bush) Relationship between discourse and background knowledge Similarities not from ratings No thematic-role cues

47. Thesis Defense, July 9, 200146 Integration Noah took the animals on the ark take ark animals Noah Input Prop agent verb place-oblique patient Parent Input Prop Child Noah Type agent Interpretation LamechProp take ark animals Noah Input Prop agent verb place-oblique patient Parent Input Prop Child Noah Type agent Interpretation ArkProp

48. Thesis Defense, July 9, 200147 Metaphor in Discourse ExperimentsMetaphor vs. Literal Reading Time Ortony et al., 1978 Inhoff et al., 1984 Shinjo & Myers, 1987 Keysar, 1990 Gibbs, 1990 Onishi & Murphy, 1993 slower same Comprehension shallow deep